Traffic Flow Prediction Using Regression and Deep Learning Approach

Lonare, Savita; Bhramaramba, R.

doi:10.1007/978-3-030-41862-5_63

Cited by 4 publications

(1 citation statement)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…LASSO regression is used to find the correlations between the pattern features and select the most critical features that are linearly related to the response. Lonare and Bhramaramba (2020) integrated the spatiotemporal correlation and compared linear regression model and LSTM model in traffic speed prediction. According to the results, LSTM model provided better performance.…”

Section: B Regression Modelmentioning

confidence: 99%

A Review of Traffic State Prediction (TSP) Methods in Intelligent Transportation Systems (ITS)

Ahanin¹,

Mustapha²,

Zolkepli³

et al. 2023

IJARBSS

View full text Add to dashboard Cite

In today's world, traffic congestion is a major problem in almost all metropolitans. This problem is even becoming more crucial due to increasing numbers of vehicles. Mobility of people, travel time duration, quality of life, transportation planning systems and traffic management are examples which bear the effects of traffic congestion The modern smart technology such as Artificial Intelligence (AI) has reduced traffic congestion by improving traffic monitoring and management technologies. These technologies require sufficient and accurate traffic data such as flow, velocity, and traffic density. Several machine learning-based methods have been proposed to predict the traffic state. Providing accurate prediction is an important stage in the successful implementation of Intelligent Transportation Systems (ITS). In this paper, we summarize the latest approaches in enhancing traffic state prediction, and possible developments in future, which potentially can transform many aspects of traffic management.

show abstract

Section: B Regression Modelmentioning

confidence: 99%